IEEE Spectrum
Efficient Parallel Algorithms for Solvent Accessible Surface Area of Proteins
IEEE Transactions on Parallel and Distributed Systems
Parallel Computation in Biological Sequence Analysis
IEEE Transactions on Parallel and Distributed Systems
Rapid Large-Scale Oligonucleotide Selection for Microarrays
CSB '02 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
dAMUSE---A new tool for denoising and blind source separation
Digital Signal Processing
Diverse accurate feature selection for microarray cancer diagnosis
Intelligent Data Analysis
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Oligonucleotide Microarrays have become powerful tools in Genetic Research, as they serve as parallel scanning mechanisms to detect the presence of genes using test probes composed of controlled segments of gene code built by masking techniques. The detection of each gene depends on the multichannel differential expression of perfectly matched segments against mismatched ones. This methodology, devised to robustify the detection process posses some interesting problems under the point of view of Genomic Signal Processing, as test probes express themselves in rather different patterns, not showing proportional expression levels for most of the segment pairs, as it would be expected. These cases may be influenced by unexpected hybridization dynamics, and are worth of being studied with a double objective: gain insight into hybridization dynamics in microarrays, and to improve microarray production and processing as well. Two methods are proposed in this paper: modelling the dynamics of expression dynamics and isolating gene expressions showing unexpected behaviour to proceed in their further classification and study.